779 lines
31 KiB
Python
779 lines
31 KiB
Python
# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import gc
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import tempfile
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import time
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import unittest
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import numpy as np
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import torch
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from diffusers import (
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AutoencoderKL,
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DDIMScheduler,
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DPMSolverMultistepScheduler,
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EulerAncestralDiscreteScheduler,
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EulerDiscreteScheduler,
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LMSDiscreteScheduler,
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PNDMScheduler,
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StableDiffusionPipeline,
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UNet2DConditionModel,
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logging,
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)
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from diffusers.utils import load_numpy, nightly, slow, torch_device
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from diffusers.utils.testing_utils import CaptureLogger, require_torch_gpu
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from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
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from ...test_pipelines_common import PipelineTesterMixin
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torch.backends.cuda.matmul.allow_tf32 = False
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class StableDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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pipeline_class = StableDiffusionPipeline
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def get_dummy_components(self):
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torch.manual_seed(0)
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unet = UNet2DConditionModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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in_channels=4,
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out_channels=4,
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down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
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up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
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cross_attention_dim=32,
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)
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scheduler = DDIMScheduler(
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beta_start=0.00085,
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beta_end=0.012,
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beta_schedule="scaled_linear",
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clip_sample=False,
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set_alpha_to_one=False,
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)
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torch.manual_seed(0)
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vae = AutoencoderKL(
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block_out_channels=[32, 64],
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in_channels=3,
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out_channels=3,
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down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
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up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
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latent_channels=4,
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)
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torch.manual_seed(0)
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text_encoder_config = CLIPTextConfig(
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bos_token_id=0,
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eos_token_id=2,
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hidden_size=32,
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intermediate_size=37,
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layer_norm_eps=1e-05,
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num_attention_heads=4,
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num_hidden_layers=5,
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pad_token_id=1,
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vocab_size=1000,
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)
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text_encoder = CLIPTextModel(text_encoder_config)
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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components = {
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"unet": unet,
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"scheduler": scheduler,
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"vae": vae,
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"text_encoder": text_encoder,
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"tokenizer": tokenizer,
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"safety_checker": None,
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"feature_extractor": None,
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}
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return components
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def get_dummy_inputs(self, device, seed=0):
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if str(device).startswith("mps"):
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generator = torch.manual_seed(seed)
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else:
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generator = torch.Generator(device=device).manual_seed(seed)
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inputs = {
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"prompt": "A painting of a squirrel eating a burger",
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"generator": generator,
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"num_inference_steps": 2,
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"guidance_scale": 6.0,
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"output_type": "numpy",
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}
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return inputs
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def test_stable_diffusion_ddim(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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sd_pipe = StableDiffusionPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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output = sd_pipe(**inputs)
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array([0.5643, 0.6017, 0.4799, 0.5267, 0.5584, 0.4641, 0.5159, 0.4963, 0.4791])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_ddim_factor_8(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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sd_pipe = StableDiffusionPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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output = sd_pipe(**inputs, height=136, width=136)
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 136, 136, 3)
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expected_slice = np.array([0.5524, 0.5626, 0.6069, 0.4727, 0.386, 0.3995, 0.4613, 0.4328, 0.4269])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_pndm(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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sd_pipe = StableDiffusionPipeline(**components)
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sd_pipe.scheduler = PNDMScheduler(skip_prk_steps=True)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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output = sd_pipe(**inputs)
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array([0.5094, 0.5674, 0.4667, 0.5125, 0.5696, 0.4674, 0.5277, 0.4964, 0.4945])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_no_safety_checker(self):
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pipe = StableDiffusionPipeline.from_pretrained(
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"hf-internal-testing/tiny-stable-diffusion-lms-pipe", safety_checker=None
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)
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assert isinstance(pipe, StableDiffusionPipeline)
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assert isinstance(pipe.scheduler, LMSDiscreteScheduler)
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assert pipe.safety_checker is None
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image = pipe("example prompt", num_inference_steps=2).images[0]
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assert image is not None
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# check that there's no error when saving a pipeline with one of the models being None
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with tempfile.TemporaryDirectory() as tmpdirname:
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pipe.save_pretrained(tmpdirname)
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pipe = StableDiffusionPipeline.from_pretrained(tmpdirname)
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# sanity check that the pipeline still works
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assert pipe.safety_checker is None
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image = pipe("example prompt", num_inference_steps=2).images[0]
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assert image is not None
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def test_stable_diffusion_k_lms(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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sd_pipe = StableDiffusionPipeline(**components)
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sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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output = sd_pipe(**inputs)
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array(
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[
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0.47082293033599854,
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0.5371589064598083,
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0.4562119245529175,
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0.5220914483070374,
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0.5733777284622192,
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0.4795039892196655,
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0.5465868711471558,
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0.5074326395988464,
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0.5042197108268738,
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]
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)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_k_euler_ancestral(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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sd_pipe = StableDiffusionPipeline(**components)
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sd_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(sd_pipe.scheduler.config)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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output = sd_pipe(**inputs)
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array(
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[
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0.4707113206386566,
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0.5372191071510315,
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0.4563021957874298,
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0.5220003724098206,
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0.5734264850616455,
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0.4794946610927582,
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0.5463782548904419,
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0.5074145197868347,
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0.504422664642334,
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]
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)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_k_euler(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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sd_pipe = StableDiffusionPipeline(**components)
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sd_pipe.scheduler = EulerDiscreteScheduler.from_config(sd_pipe.scheduler.config)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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output = sd_pipe(**inputs)
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array(
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[
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0.47082313895225525,
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0.5371587872505188,
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0.4562119245529175,
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0.5220913887023926,
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0.5733776688575745,
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0.47950395941734314,
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0.546586811542511,
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0.5074326992034912,
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0.5042197108268738,
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]
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)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_vae_slicing(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config)
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sd_pipe = StableDiffusionPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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image_count = 4
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inputs = self.get_dummy_inputs(device)
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inputs["prompt"] = [inputs["prompt"]] * image_count
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output_1 = sd_pipe(**inputs)
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# make sure sliced vae decode yields the same result
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sd_pipe.enable_vae_slicing()
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inputs = self.get_dummy_inputs(device)
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inputs["prompt"] = [inputs["prompt"]] * image_count
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output_2 = sd_pipe(**inputs)
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# there is a small discrepancy at image borders vs. full batch decode
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assert np.abs(output_2.images.flatten() - output_1.images.flatten()).max() < 3e-3
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def test_stable_diffusion_negative_prompt(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
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sd_pipe = StableDiffusionPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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negative_prompt = "french fries"
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output = sd_pipe(**inputs, negative_prompt=negative_prompt)
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array(
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[
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0.5108221173286438,
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0.5688379406929016,
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0.4685141146183014,
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0.5098261833190918,
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0.5657756328582764,
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0.4631010890007019,
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0.5226285457611084,
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0.49129390716552734,
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0.4899061322212219,
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]
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)
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_num_images_per_prompt(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
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sd_pipe = StableDiffusionPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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prompt = "A painting of a squirrel eating a burger"
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# test num_images_per_prompt=1 (default)
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images = sd_pipe(prompt, num_inference_steps=2, output_type="np").images
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assert images.shape == (1, 64, 64, 3)
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# test num_images_per_prompt=1 (default) for batch of prompts
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batch_size = 2
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images = sd_pipe([prompt] * batch_size, num_inference_steps=2, output_type="np").images
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assert images.shape == (batch_size, 64, 64, 3)
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# test num_images_per_prompt for single prompt
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num_images_per_prompt = 2
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images = sd_pipe(
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prompt, num_inference_steps=2, output_type="np", num_images_per_prompt=num_images_per_prompt
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).images
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assert images.shape == (num_images_per_prompt, 64, 64, 3)
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# test num_images_per_prompt for batch of prompts
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batch_size = 2
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images = sd_pipe(
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[prompt] * batch_size, num_inference_steps=2, output_type="np", num_images_per_prompt=num_images_per_prompt
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).images
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assert images.shape == (batch_size * num_images_per_prompt, 64, 64, 3)
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def test_stable_diffusion_long_prompt(self):
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components = self.get_dummy_components()
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components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config)
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sd_pipe = StableDiffusionPipeline(**components)
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sd_pipe = sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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do_classifier_free_guidance = True
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negative_prompt = None
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num_images_per_prompt = 1
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logger = logging.get_logger("diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion")
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prompt = 25 * "@"
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with CaptureLogger(logger) as cap_logger_3:
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text_embeddings_3 = sd_pipe._encode_prompt(
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prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
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)
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prompt = 100 * "@"
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with CaptureLogger(logger) as cap_logger:
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text_embeddings = sd_pipe._encode_prompt(
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prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
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)
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negative_prompt = "Hello"
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with CaptureLogger(logger) as cap_logger_2:
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text_embeddings_2 = sd_pipe._encode_prompt(
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prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
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)
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assert text_embeddings_3.shape == text_embeddings_2.shape == text_embeddings.shape
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assert text_embeddings.shape[1] == 77
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assert cap_logger.out == cap_logger_2.out
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# 100 - 77 + 1 (BOS token) + 1 (EOS token) = 25
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assert cap_logger.out.count("@") == 25
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assert cap_logger_3.out == ""
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def test_stable_diffusion_height_width_opt(self):
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components = self.get_dummy_components()
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components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config)
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sd_pipe = StableDiffusionPipeline(**components)
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sd_pipe = sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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prompt = "hey"
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output = sd_pipe(prompt, num_inference_steps=1, output_type="np")
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image_shape = output.images[0].shape[:2]
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assert image_shape == (64, 64)
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output = sd_pipe(prompt, num_inference_steps=1, height=96, width=96, output_type="np")
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image_shape = output.images[0].shape[:2]
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assert image_shape == (96, 96)
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config = dict(sd_pipe.unet.config)
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config["sample_size"] = 96
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sd_pipe.unet = UNet2DConditionModel.from_config(config).to(torch_device)
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output = sd_pipe(prompt, num_inference_steps=1, output_type="np")
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image_shape = output.images[0].shape[:2]
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assert image_shape == (192, 192)
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@slow
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@require_torch_gpu
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class StableDiffusionPipelineSlowTests(unittest.TestCase):
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def tearDown(self):
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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def get_inputs(self, device, dtype=torch.float32, seed=0):
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generator = torch.Generator(device=device).manual_seed(seed)
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latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
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latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
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inputs = {
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"prompt": "a photograph of an astronaut riding a horse",
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"latents": latents,
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"generator": generator,
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"num_inference_steps": 3,
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"guidance_scale": 7.5,
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"output_type": "numpy",
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}
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return inputs
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def test_stable_diffusion_1_1_pndm(self):
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sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1")
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sd_pipe = sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_inputs(torch_device)
|
|
image = sd_pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1].flatten()
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.43625, 0.43554, 0.36670, 0.40660, 0.39703, 0.38658, 0.43936, 0.43557, 0.40592])
|
|
assert np.abs(image_slice - expected_slice).max() < 1e-4
|
|
|
|
def test_stable_diffusion_1_4_pndm(self):
|
|
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
|
|
sd_pipe = sd_pipe.to(torch_device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_inputs(torch_device)
|
|
image = sd_pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1].flatten()
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.57400, 0.47841, 0.31625, 0.63583, 0.58306, 0.55056, 0.50825, 0.56306, 0.55748])
|
|
assert np.abs(image_slice - expected_slice).max() < 1e-4
|
|
|
|
def test_stable_diffusion_ddim(self):
|
|
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
|
|
sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)
|
|
sd_pipe = sd_pipe.to(torch_device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_inputs(torch_device)
|
|
image = sd_pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1].flatten()
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.38019, 0.28647, 0.27321, 0.40377, 0.38290, 0.35446, 0.39218, 0.38165, 0.42239])
|
|
assert np.abs(image_slice - expected_slice).max() < 1e-4
|
|
|
|
def test_stable_diffusion_lms(self):
|
|
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
|
|
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
|
|
sd_pipe = sd_pipe.to(torch_device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_inputs(torch_device)
|
|
image = sd_pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1].flatten()
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.10542, 0.09620, 0.07332, 0.09015, 0.09382, 0.07597, 0.08496, 0.07806, 0.06455])
|
|
assert np.abs(image_slice - expected_slice).max() < 1e-4
|
|
|
|
def test_stable_diffusion_dpm(self):
|
|
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", safety_checker=None)
|
|
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
|
|
sd_pipe = sd_pipe.to(torch_device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_inputs(torch_device)
|
|
image = sd_pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1].flatten()
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.03503, 0.03494, 0.01087, 0.03128, 0.02552, 0.00803, 0.00742, 0.00372, 0.00000])
|
|
assert np.abs(image_slice - expected_slice).max() < 1e-4
|
|
|
|
def test_stable_diffusion_attention_slicing(self):
|
|
torch.cuda.reset_peak_memory_stats()
|
|
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
# enable attention slicing
|
|
pipe.enable_attention_slicing()
|
|
inputs = self.get_inputs(torch_device, dtype=torch.float16)
|
|
image_sliced = pipe(**inputs).images
|
|
|
|
mem_bytes = torch.cuda.max_memory_allocated()
|
|
torch.cuda.reset_peak_memory_stats()
|
|
# make sure that less than 3.75 GB is allocated
|
|
assert mem_bytes < 3.75 * 10**9
|
|
|
|
# disable slicing
|
|
pipe.disable_attention_slicing()
|
|
inputs = self.get_inputs(torch_device, dtype=torch.float16)
|
|
image = pipe(**inputs).images
|
|
|
|
# make sure that more than 3.75 GB is allocated
|
|
mem_bytes = torch.cuda.max_memory_allocated()
|
|
assert mem_bytes > 3.75 * 10**9
|
|
assert np.abs(image_sliced - image).max() < 1e-3
|
|
|
|
def test_stable_diffusion_vae_slicing(self):
|
|
torch.cuda.reset_peak_memory_stats()
|
|
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
# enable vae slicing
|
|
pipe.enable_vae_slicing()
|
|
inputs = self.get_inputs(torch_device, dtype=torch.float16)
|
|
inputs["prompt"] = [inputs["prompt"]] * 4
|
|
inputs["latents"] = torch.cat([inputs["latents"]] * 4)
|
|
image_sliced = pipe(**inputs).images
|
|
|
|
mem_bytes = torch.cuda.max_memory_allocated()
|
|
torch.cuda.reset_peak_memory_stats()
|
|
# make sure that less than 4 GB is allocated
|
|
assert mem_bytes < 4e9
|
|
|
|
# disable vae slicing
|
|
pipe.disable_vae_slicing()
|
|
inputs = self.get_inputs(torch_device, dtype=torch.float16)
|
|
inputs["prompt"] = [inputs["prompt"]] * 4
|
|
inputs["latents"] = torch.cat([inputs["latents"]] * 4)
|
|
image = pipe(**inputs).images
|
|
|
|
# make sure that more than 4 GB is allocated
|
|
mem_bytes = torch.cuda.max_memory_allocated()
|
|
assert mem_bytes > 4e9
|
|
# There is a small discrepancy at the image borders vs. a fully batched version.
|
|
assert np.abs(image_sliced - image).max() < 4e-3
|
|
|
|
def test_stable_diffusion_fp16_vs_autocast(self):
|
|
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_inputs(torch_device, dtype=torch.float16)
|
|
image_fp16 = pipe(**inputs).images
|
|
|
|
with torch.autocast(torch_device):
|
|
inputs = self.get_inputs(torch_device)
|
|
image_autocast = pipe(**inputs).images
|
|
|
|
# Make sure results are close enough
|
|
diff = np.abs(image_fp16.flatten() - image_autocast.flatten())
|
|
# They ARE different since ops are not run always at the same precision
|
|
# however, they should be extremely close.
|
|
assert diff.mean() < 2e-2
|
|
|
|
def test_stable_diffusion_intermediate_state(self):
|
|
number_of_steps = 0
|
|
|
|
def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
|
|
callback_fn.has_been_called = True
|
|
nonlocal number_of_steps
|
|
number_of_steps += 1
|
|
if step == 1:
|
|
latents = latents.detach().cpu().numpy()
|
|
assert latents.shape == (1, 4, 64, 64)
|
|
latents_slice = latents[0, -3:, -3:, -1]
|
|
expected_slice = np.array([-0.5713, -0.3018, -0.9814, 0.04663, -0.879, 0.76, -1.734, 0.1044, 1.161])
|
|
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-3
|
|
elif step == 2:
|
|
latents = latents.detach().cpu().numpy()
|
|
assert latents.shape == (1, 4, 64, 64)
|
|
latents_slice = latents[0, -3:, -3:, -1]
|
|
expected_slice = np.array([-0.1885, -0.3022, -1.012, -0.514, -0.477, 0.6143, -0.9336, 0.6553, 1.453])
|
|
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-2
|
|
|
|
callback_fn.has_been_called = False
|
|
|
|
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
inputs = self.get_inputs(torch_device, dtype=torch.float16)
|
|
pipe(**inputs, callback=callback_fn, callback_steps=1)
|
|
assert callback_fn.has_been_called
|
|
assert number_of_steps == inputs["num_inference_steps"]
|
|
|
|
def test_stable_diffusion_low_cpu_mem_usage(self):
|
|
pipeline_id = "CompVis/stable-diffusion-v1-4"
|
|
|
|
start_time = time.time()
|
|
pipeline_low_cpu_mem_usage = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16)
|
|
pipeline_low_cpu_mem_usage.to(torch_device)
|
|
low_cpu_mem_usage_time = time.time() - start_time
|
|
|
|
start_time = time.time()
|
|
_ = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16, low_cpu_mem_usage=False)
|
|
normal_load_time = time.time() - start_time
|
|
|
|
assert 2 * low_cpu_mem_usage_time < normal_load_time
|
|
|
|
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.reset_max_memory_allocated()
|
|
torch.cuda.reset_peak_memory_stats()
|
|
|
|
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing(1)
|
|
pipe.enable_sequential_cpu_offload()
|
|
|
|
inputs = self.get_inputs(torch_device, dtype=torch.float16)
|
|
_ = pipe(**inputs)
|
|
|
|
mem_bytes = torch.cuda.max_memory_allocated()
|
|
# make sure that less than 2.8 GB is allocated
|
|
assert mem_bytes < 2.8 * 10**9
|
|
|
|
|
|
@nightly
|
|
@require_torch_gpu
|
|
class StableDiffusionPipelineNightlyTests(unittest.TestCase):
|
|
def tearDown(self):
|
|
super().tearDown()
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
def get_inputs(self, device, dtype=torch.float32, seed=0):
|
|
generator = torch.Generator(device=device).manual_seed(seed)
|
|
latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
|
|
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
|
|
inputs = {
|
|
"prompt": "a photograph of an astronaut riding a horse",
|
|
"latents": latents,
|
|
"generator": generator,
|
|
"num_inference_steps": 50,
|
|
"guidance_scale": 7.5,
|
|
"output_type": "numpy",
|
|
}
|
|
return inputs
|
|
|
|
def test_stable_diffusion_1_4_pndm(self):
|
|
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(torch_device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_inputs(torch_device)
|
|
image = sd_pipe(**inputs).images[0]
|
|
|
|
expected_image = load_numpy(
|
|
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
|
|
"/stable_diffusion_text2img/stable_diffusion_1_4_pndm.npy"
|
|
)
|
|
max_diff = np.abs(expected_image - image).max()
|
|
assert max_diff < 1e-3
|
|
|
|
def test_stable_diffusion_1_5_pndm(self):
|
|
sd_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to(torch_device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_inputs(torch_device)
|
|
image = sd_pipe(**inputs).images[0]
|
|
|
|
expected_image = load_numpy(
|
|
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
|
|
"/stable_diffusion_text2img/stable_diffusion_1_5_pndm.npy"
|
|
)
|
|
max_diff = np.abs(expected_image - image).max()
|
|
assert max_diff < 1e-3
|
|
|
|
def test_stable_diffusion_ddim(self):
|
|
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(torch_device)
|
|
sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_inputs(torch_device)
|
|
image = sd_pipe(**inputs).images[0]
|
|
|
|
expected_image = load_numpy(
|
|
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
|
|
"/stable_diffusion_text2img/stable_diffusion_1_4_ddim.npy"
|
|
)
|
|
max_diff = np.abs(expected_image - image).max()
|
|
assert max_diff < 1e-3
|
|
|
|
def test_stable_diffusion_lms(self):
|
|
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(torch_device)
|
|
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_inputs(torch_device)
|
|
image = sd_pipe(**inputs).images[0]
|
|
|
|
expected_image = load_numpy(
|
|
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
|
|
"/stable_diffusion_text2img/stable_diffusion_1_4_lms.npy"
|
|
)
|
|
max_diff = np.abs(expected_image - image).max()
|
|
assert max_diff < 1e-3
|
|
|
|
def test_stable_diffusion_euler(self):
|
|
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(torch_device)
|
|
sd_pipe.scheduler = EulerDiscreteScheduler.from_config(sd_pipe.scheduler.config)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_inputs(torch_device)
|
|
image = sd_pipe(**inputs).images[0]
|
|
|
|
expected_image = load_numpy(
|
|
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
|
|
"/stable_diffusion_text2img/stable_diffusion_1_4_euler.npy"
|
|
)
|
|
max_diff = np.abs(expected_image - image).max()
|
|
assert max_diff < 1e-3
|
|
|
|
def test_stable_diffusion_dpm(self):
|
|
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(torch_device)
|
|
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_inputs(torch_device)
|
|
inputs["num_inference_steps"] = 25
|
|
image = sd_pipe(**inputs).images[0]
|
|
|
|
expected_image = load_numpy(
|
|
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
|
|
"/stable_diffusion_text2img/stable_diffusion_1_4_dpm_multi.npy"
|
|
)
|
|
max_diff = np.abs(expected_image - image).max()
|
|
assert max_diff < 1e-3
|